# Towards A Fairer Landmark Recognition Dataset

arXiv: https://arxiv.org/abs/2108.08874 · HTML: https://mlsanity.com/paper/2108.08874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)

## Built on by (most cited first, 9 of 9)

- [No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models](https://mlsanity.com/paper/2405.13777/) (2024)
- [From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models](https://mlsanity.com/paper/2407.00263/) (2024)
- [Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies](https://mlsanity.com/paper/2304.05391/) (2023)
- [2nd Place Solution to Google Landmark Retrieval 2021](https://mlsanity.com/paper/2110.04294/) (2021)
- [Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost](https://mlsanity.com/paper/2403.07687/) (2024)
- [Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers](https://mlsanity.com/paper/2110.03786/) (2021)
- [3rd Place Solution to Google Landmark Recognition Competition 2021](https://mlsanity.com/paper/2110.02794/) (2021)
- [Google Landmark Retrieval 2021 Competition Third Place Solution](https://mlsanity.com/paper/2110.04619/) (2021)
- [MABNet: Master Assistant Buddy Network with Hybrid Learning for Image Retrieval](https://mlsanity.com/paper/2303.03050/) (2023)

## Abstract (arXiv)

We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible from web repositories, we instead argue that such approaches can lead to biased data. To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population. These relevances are estimated by combining anonymized Google Maps user contribution statistics with the contributors' demographic information. We present a stratification approach and analysis which leads to a much fairer coverage of the world, compared to existing datasets. The resulting datasets are used to evaluate computer vision models as part of the the Google Landmark Recognition and RetrievalChallenges 2021.